ETL: Ideias de projetos para estudo
No artigo anterior, listei opções gratuitas e públicas de fontes de dados para projetos de ETL. Neste artigo, vou demonstrar como você pode solicitar ao chatGPT que te surgira algumas ideias para estudo.
Prompt para o chatgpt:
Act as a Senior Data Engineer and provide a list of ideas of ETL pipelines projects for a Junior Data Engineer
{Rules}
> Public API: If possible, include a list of Public APIs that could be used as data source to be analyzed
> Free dataset: If possible, include a list of Free dataset that could be used as data source to be analyzed
> Analysis: Be specific on what kind of analysis could be done.
O que ele sugeriu:
1. Social Media Sentiment Analysis Pipeline:
- Public API: Twitter API
- Analysis: Extract tweets using specific keywords, perform sentiment analysis on the text using Natural Language Processing (NLP) techniques, and store the results for further analysis.
2. Weather Data Processing Pipeline:
- Public API: OpenWeatherMap API
- Analysis: Fetch weather data for specific locations, clean and transform the data, calculate aggregate statistics like average temperature, humidity, or precipitation, and store the processed data for further analysis.
3. Sales Data Integration Pipeline:
- Free Dataset: Publicly available sales data from e-commerce platforms or retail chains
- Analysis: Fetch and consolidate sales data from multiple sources, cleanse and transform the data, perform sales trend analysis, identify popular products or customer demographics, and generate insightful reports.
4. Log Analytics Pipeline:
- Free Dataset: Apache/Nginx web server logs or application logs
- Analysis: Ingest log data, parse and structure logs, extract relevant information, perform log analysis for error detection, security monitoring, or performance optimization, and generate actionable insights.
5. Stock Market Data Pipeline:
- Public API: Alpha Vantage API or Yahoo Finance API
- Analysis: Fetch historical stock market data for specific stocks, perform data cleansing and normalization, calculate technical indicators like moving averages or MACD, analyze stock performance, identify trends, and generate visualizations.
6. IoT Sensor Data Processing Pipeline:
- Free Dataset: Generated IoT sensor data or public IoT datasets
- Analysis: Ingest sensor data streams, clean and validate the data, apply statistical analysis or anomaly detection techniques, derive meaningful insights, and store the processed data for real-time or batch analysis.
7. Customer Behavior Tracking Pipeline:
- Public API: Google Analytics API or Mixpanel API
- Analysis: Extract customer behavioral data from web analytics tools, clean and transform the data, perform user segmentation, analyze user journey and clickstream patterns, identify high-value customer segments, and generate reports for marketing or product teams.
8. Healthcare Data Integration Pipeline:
- Free Dataset: Publicly available healthcare datasets like CDC or WHO datasets
- Analysis: Integrate disparate healthcare data sources, perform data cleansing and transformation, calculate healthcare statistics like disease prevalence, mortality rates, or vaccination coverage, identify patterns or correlations, and generate insights to support healthcare decisions.
9. Flight Data Processing Pipeline:
- Public API: FlightAware API or Aviationstack API
- Analysis: Fetch flight data for specific routes, airports, or airlines, clean and enrich the data, calculate metrics like on-time performance, flight delays, or air traffic, analyze route or airline profitability, and generate dashboards or reports for airlines or aviation companies.
10. Energy Consumption Analysis Pipeline:
- Public API: Energy Information Administration (EIA) API
- Analysis: Extract energy consumption data for specific regions, clean and aggregate the data, perform time series analysis, identify energy usage patterns, analyze peak demand periods, and generate insights for energy efficiency measures or sustainability initiatives.
Compartilhe nos comentários outras ideias :)
Bons estudos!
